Overview / Usage

Project Overview:

This project focuses on developing a predictive model for solar power generation using historical weather data and time-series analysis. The goal is to improve the accuracy of renewable energy forecasting, enabling better grid management and increased reliance on solar power.

Problems Being Solved:

1. Inaccurate Forecasting: Existing solar power generation forecasting models often rely on simplistic approaches, leading to inaccurate predictions.

2. Intermittent Energy Supply: Solar power generation is intermittent, making it challenging to ensure a stable energy supply.

3. Grid Management Complexity: Inaccurate forecasting can lead to grid management complexities, including energy waste and potential power outages.

Usage in Production:

This project's outcome can be used in various industries, including:

1. Renewable Energy Companies: To improve the accuracy of solar power generation forecasting, enabling better grid management and increased reliance on renewable energy.

2. Grid Operators: To optimize grid operations, reduce energy waste, and prevent potential power outages.

3. Energy Trading Platforms: To provide accurate solar power generation forecasts, enabling better energy trading and risk management.

Methodology / Approach

Methodology

This project employs a data-driven approach to predict solar power generation using historical weather data and time-series analysis. The methodology involves:

Data Collection

1. Historical Weather Data: Collecting historical weather data from reliable sources, such as national weather services or weather APIs.

2. Solar Power Generation Data: Collecting historical solar power generation data from solar panels or energy monitoring systems.

Data Preprocessing

1. Data Cleaning: Handling missing values, outliers, and noisy data using techniques like imputation, normalization, and feature scaling.

2. Feature Engineering: Extracting relevant features from the data, such as time-series decomposition, trend analysis, and weather-based feature extraction.

Model Development

1. Machine Learning Algorithms: Implementing and comparing various machine learning algorithms, including ARIMA, LSTM, and Prophet.

2. Hyperparameter Tuning: Optimizing model hyperparameters using techniques like grid search, random search, and Bayesian optimization.

Model Evaluation

1. Metrics: Evaluating model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R-squared).

2. Cross-Validation: Validating model performance using techniques like k-fold cross-validation and walk-forward optimization.

Technologies Used

1. Python: Programming language for data analysis, machine learning, and visualization.

2. Pandas: Library for data manipulation, analysis, and visualization.

3. NumPy: Library for numerical computing and data analysis.

4. Scikit-learn: Machine learning library for data preprocessing, feature selection, and model development.

5. TensorFlow: Open-source machine learning framework for building and training neural networks.

6. Prophet: Open-source software for forecasting time series data.

7. Matplotlib: Library for data visualization and presentation.

8. Seaborn: Library for statistical data visualization.

9. Jupyter Notebook: Interactive environment for data analysis, visualization, and prototyping.

10. Intel Core i7: High-performance processor for data processing and analysis.

Repository

https://www.kaggle.com/code/zengamer/solar-power-generation-prediction